
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
A__							: Any     =       logging.get_logger("""transformers.models.speecht5""")

A__							: Union[str, Any]     =       {
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
A__							: Tuple     =       {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
A__							: Tuple     =       {
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
A__							: int     =       {
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
A__							: int     =       {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
A__							: Dict     =       {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
A__							: Any     =       {
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
A__							: Tuple     =       {
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
A__							: List[Any]     =       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A__							: Dict     =       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__							: List[str]     =       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A__							: Optional[Any]     =       []
A__							: Optional[Any]     =       [
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
A__							: List[Any]     =       IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
A__							: str     =       IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
A__							: Any     =       IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]



def   _a  ( __UpperCamelCase							:	Union[str, Any]						,__UpperCamelCase							:	Optional[Any]						,__UpperCamelCase							:	str						,__UpperCamelCase							:	List[str]						,__UpperCamelCase							:	Union[str, Any]     ):





			for attribute in key.split('''.'''     ):
						lowerCAmelCase__			:     Optional[Any]					   =      getattr(A_						,A_     )

			if weight_type is not None:
						lowerCAmelCase__			:     Union[str, Any]					   =      getattr(A_						,A_     ).shape
			else:
						lowerCAmelCase__			:     str					   =      hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
						    f''' {value.shape} for {full_name}'''     )

			if weight_type == "weight":
						lowerCAmelCase__			:     int					   =      value
			elif weight_type == "weight_g":
						lowerCAmelCase__			:     Any					   =      value
			elif weight_type == "weight_v":
						lowerCAmelCase__			:     str					   =      value
			elif weight_type == "bias":
						lowerCAmelCase__			:     Optional[int]					   =      value
			elif weight_type == "running_mean":
						lowerCAmelCase__			:     Union[str, Any]					   =      value
			elif weight_type == "running_var":
						lowerCAmelCase__			:     List[Any]					   =      value
			elif weight_type == "num_batches_tracked":
						lowerCAmelCase__			:     Tuple					   =      value
			else:
						lowerCAmelCase__			:     Dict					   =      value

			logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''     )



def   _a  ( __UpperCamelCase							:	Dict						,__UpperCamelCase							:	int     ):





			for key in ignore_keys:
						if key.endswith('''.*'''     ):
									if name.startswith(key[:-1]     ):
												return True
						elif ".*." in key:
									lowerCAmelCase__   ,					lowerCAmelCase__			:     Tuple					   =      key.split('''.*.'''     )
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False



def   _a  ( __UpperCamelCase							:	List[str]						,__UpperCamelCase							:	Union[str, Any]						,__UpperCamelCase							:	Any     ):
			lowerCAmelCase__			:     Optional[Any]					   =      []

			if task == "s2t":
						lowerCAmelCase__			:     Dict					   =      hf_model.speechta.encoder.prenet.feature_encoder
						lowerCAmelCase__			:     Optional[int]					   =      MAPPING_S2T
						lowerCAmelCase__			:     Any					   =      IGNORE_KEYS_S2T
			elif task == "t2s":
						lowerCAmelCase__			:     Any					   =      None
						lowerCAmelCase__			:     int					   =      MAPPING_T2S
						lowerCAmelCase__			:     List[str]					   =      IGNORE_KEYS_T2S
			elif task == "s2s":
						lowerCAmelCase__			:     str					   =      hf_model.speechta.encoder.prenet.feature_encoder
						lowerCAmelCase__			:     List[Any]					   =      MAPPING_S2S
						lowerCAmelCase__			:     Tuple					   =      IGNORE_KEYS_S2S
			else:
						raise ValueError(f'''Unsupported task: {task}'''     )

			for name, value in fairseq_dict.items():
						if should_ignore(A_						,A_     ):
									logger.info(f'''{name} was ignored'''     )
									continue

						lowerCAmelCase__			:     Union[str, Any]					   =      False
						if "conv_layers" in name:
									load_conv_layer(
									    A_						,A_						,A_						,A_						,hf_model.config.feat_extract_norm == '''group'''						,)
									lowerCAmelCase__			:     Union[str, Any]					   =      True
						else:
									for key, mapped_key in MAPPING.items():
												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

												if "*" in key:
															lowerCAmelCase__   ,					lowerCAmelCase__			:     Dict					   =      key.split('''.*.'''     )
															if prefix in name and suffix in name:
																		lowerCAmelCase__			:     Optional[int]					   =      suffix

																		    # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															lowerCAmelCase__			:     Optional[int]					   =      True
															if "*" in mapped_key:
																		lowerCAmelCase__			:     List[str]					   =      name.split(A_     )[0].split('''.'''     )[-2]
																		lowerCAmelCase__			:     int					   =      mapped_key.replace('''*'''						,A_     )
															if "weight_g" in name:
																		lowerCAmelCase__			:     Optional[int]					   =      '''weight_g'''
															elif "weight_v" in name:
																		lowerCAmelCase__			:     Any					   =      '''weight_v'''
															elif "bias" in name:
																		lowerCAmelCase__			:     Tuple					   =      '''bias'''
															elif "weight" in name:
																		lowerCAmelCase__			:     Tuple					   =      '''weight'''
															elif "running_mean" in name:
																		lowerCAmelCase__			:     Dict					   =      '''running_mean'''
															elif "running_var" in name:
																		lowerCAmelCase__			:     Tuple					   =      '''running_var'''
															elif "num_batches_tracked" in name:
																		lowerCAmelCase__			:     Dict					   =      '''num_batches_tracked'''
															else:
																		lowerCAmelCase__			:     Any					   =      None
															set_recursively(A_						,A_						,A_						,A_						,A_     )
												continue
						if not is_used:
									unused_weights.append(A_     )

			logger.warning(f'''Unused weights: {unused_weights}'''     )



def   _a  ( __UpperCamelCase							:	Any						,__UpperCamelCase							:	List[str]						,__UpperCamelCase							:	List[str]						,__UpperCamelCase							:	int						,__UpperCamelCase							:	Tuple     ):
			lowerCAmelCase__			:     List[Any]					   =      full_name.split('''conv_layers.'''     )[-1]
			lowerCAmelCase__			:     List[str]					   =      name.split('''.'''     )
			lowerCAmelCase__			:     List[Any]					   =      int(items[0]     )
			lowerCAmelCase__			:     Dict					   =      int(items[1]     )

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''     )
									lowerCAmelCase__			:     List[str]					   =      value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''     )
									lowerCAmelCase__			:     Dict					   =      value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''     )
									lowerCAmelCase__			:     Optional[int]					   =      value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''     )
									lowerCAmelCase__			:     Any					   =      value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
			else:
						unused_weights.append(A_     )



@torch.no_grad()
def   _a  ( __UpperCamelCase							:	Optional[int]						,__UpperCamelCase							:	List[Any]						,__UpperCamelCase							:	int						,__UpperCamelCase							:	Union[str, Any]=None						,__UpperCamelCase							:	Tuple=None						,__UpperCamelCase							:	Optional[int]=None						,):





			if config_path is not None:
						lowerCAmelCase__			:     int					   =      SpeechTaConfig.from_pretrained(A_     )
			else:
						lowerCAmelCase__			:     List[str]					   =      SpeechTaConfig()

			if task == "s2t":
						lowerCAmelCase__			:     Union[str, Any]					   =      config.max_text_positions
						lowerCAmelCase__			:     int					   =      SpeechTaForSpeechToText(A_     )
			elif task == "t2s":
						lowerCAmelCase__			:     List[Any]					   =      1876
						lowerCAmelCase__			:     str					   =      600
						lowerCAmelCase__			:     Optional[Any]					   =      config.max_speech_positions
						lowerCAmelCase__			:     List[str]					   =      SpeechTaForTextToSpeech(A_     )
			elif task == "s2s":
						lowerCAmelCase__			:     List[str]					   =      1876
						lowerCAmelCase__			:     Optional[Any]					   =      config.max_speech_positions
						lowerCAmelCase__			:     Union[str, Any]					   =      SpeechTaForSpeechToSpeech(A_     )
			else:
						raise ValueError(f'''Unknown task name: {task}'''     )

			if vocab_path:
						lowerCAmelCase__			:     Optional[int]					   =      SpeechTaTokenizer(A_						,model_max_length=config.max_text_positions     )

						# Mask token behaves like a normal word, i.e. include the space before it
						lowerCAmelCase__			:     List[str]					   =      AddedToken('''<mask>'''						,lstrip=A_						,rstrip=A_     )
						lowerCAmelCase__			:     List[str]					   =      mask_token
						tokenizer.add_special_tokens({'''mask_token''': mask_token}     )
						tokenizer.add_tokens(['''<ctc_blank>''']     )

			lowerCAmelCase__			:     int					   =      SpeechTaFeatureExtractor()
			lowerCAmelCase__			:     int					   =      SpeechTaProcessor(tokenizer=A_						,feature_extractor=A_     )
			processor.save_pretrained(A_     )

			lowerCAmelCase__			:     List[Any]					   =      torch.load(A_     )
			recursively_load_weights(fairseq_checkpoint['''model''']						,A_						,A_     )

			model.save_pretrained(A_     )

			if repo_id:
						print('''Pushing to the hub...'''     )
						processor.push_to_hub(A_     )
						model.push_to_hub(A_     )


if __name__ == "__main__":
							A__							: List[Any]     =       argparse.ArgumentParser()
							parser.add_argument(
							    """--task""",
							    default="""s2t""",
							    type=str,
							    help="""Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.""",
							)
							parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
							)

							A__							: int     =       parser.parse_args()
							convert_speechta_checkpoint(
							    args.task,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.vocab_path,
							    args.push_to_hub,
							)





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

